Robotic System for Blood Serum Aliquoting Based on a Neural Network Model of Machine Vision
Sergey Khalapyan,
Larisa Rybak,
Vasiliy Nebolsin,
Dmitry Malyshev,
Anna Nozdracheva,
Tatyana Semenenko,
Dmitry Gavrilov
Affiliations
Sergey Khalapyan
Department of Automated and Information Control Systems, Stary Oskol Technological Institute n.a. A.A. Ugarov NUST MISiS, 42, Makarenko Mkr., 309516 Stary Oskol, Russia
Larisa Rybak
Research Institute Robotics and Control Systems, Belgorod State Technological University n.a. V.G. Shukhov, Kostyukova 46, 308012 Belgorod, Russia
Vasiliy Nebolsin
Department of Automated and Information Control Systems, Stary Oskol Technological Institute n.a. A.A. Ugarov NUST MISiS, 42, Makarenko Mkr., 309516 Stary Oskol, Russia
Dmitry Malyshev
Research Institute Robotics and Control Systems, Belgorod State Technological University n.a. V.G. Shukhov, Kostyukova 46, 308012 Belgorod, Russia
Anna Nozdracheva
Research Institute Robotics and Control Systems, Belgorod State Technological University n.a. V.G. Shukhov, Kostyukova 46, 308012 Belgorod, Russia
Tatyana Semenenko
Department of Epidemiology, Research Institute of Epidemiology and Microbiology n.a. N.F. Gamalei, Russian Academy of Medical Scences, 18, Gamaleya Str., 123098 Moscow, Russia
Dmitry Gavrilov
Research Institute Robotics and Control Systems, Belgorod State Technological University n.a. V.G. Shukhov, Kostyukova 46, 308012 Belgorod, Russia
The quality of the diagnostic information obtained in the course of laboratory studies depends on the accuracy of compliance with the regulations for the necessary work. The process of aliquoting blood serum requires immersing the pipette to different depths depending on the boundary level between blood phases. A vision system can be used to determine this depth during automated aliquoting using various algorithms. As part of the work, two recognition algorithms are synthesized, one of which is based on the use of the HSV color palette, the other is based on the convolutional neural network. In the Python language, software systems have been developed that implement the ability of a vision system to recognize blood in test tubes. The developed methods are supposed to be used for aliquoting biosamples using a delta robot in a multirobotic system, which will increase the productivity of ongoing biomedical research through the use of new technical solutions and principles of intelligent robotics. The visualized results of the work of the considered programs are presented and a comparative analysis of the quality of recognition is carried out.
